Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations2263680
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory386.4 MiB
Average record size in memory179.0 B

Variable types

Categorical4
Text2
Boolean1
Numeric8
DateTime2

Alerts

country_code is highly overall correlated with payment_methodHigh correlation
num_cancelled_orders_last_50days is highly overall correlated with num_refund_orders_last_50daysHigh correlation
num_orders_last_50days is highly overall correlated with num_refund_orders_last_50days and 1 other fieldsHigh correlation
num_refund_orders_last_50days is highly overall correlated with num_cancelled_orders_last_50days and 1 other fieldsHigh correlation
payment_method is highly overall correlated with country_codeHigh correlation
total_payment_last_50days is highly overall correlated with num_orders_last_50daysHigh correlation
mobile_verified is highly imbalanced (99.1%) Imbalance
collect_type is highly imbalanced (90.2%) Imbalance
num_orders_last_50days has 86058 (3.8%) zeros Zeros
num_cancelled_orders_last_50days has 1485772 (65.6%) zeros Zeros
num_refund_orders_last_50days has 1318793 (58.3%) zeros Zeros
total_payment_last_50days has 54054 (2.4%) zeros Zeros
refund_value has 876848 (38.7%) zeros Zeros

Reproduction

Analysis started2025-01-27 04:38:05.806309
Analysis finished2025-01-27 04:40:07.760018
Duration2 minutes and 1.95 second
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

country_code
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
MY
858381 
PH
670952 
PK
440078 
TH
151871 
BD
142398 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4527360
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBD
2nd rowBD
3rd rowBD
4th rowBD
5th rowBD

Common Values

ValueCountFrequency (%)
MY 858381
37.9%
PH 670952
29.6%
PK 440078
19.4%
TH 151871
 
6.7%
BD 142398
 
6.3%

Length

2025-01-27T12:40:08.045739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T12:40:08.184701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
my 858381
37.9%
ph 670952
29.6%
pk 440078
19.4%
th 151871
 
6.7%
bd 142398
 
6.3%

Most occurring characters

ValueCountFrequency (%)
P 1111030
24.5%
M 858381
19.0%
Y 858381
19.0%
H 822823
18.2%
K 440078
 
9.7%
T 151871
 
3.4%
B 142398
 
3.1%
D 142398
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4527360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1111030
24.5%
M 858381
19.0%
Y 858381
19.0%
H 822823
18.2%
K 440078
 
9.7%
T 151871
 
3.4%
B 142398
 
3.1%
D 142398
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4527360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1111030
24.5%
M 858381
19.0%
Y 858381
19.0%
H 822823
18.2%
K 440078
 
9.7%
T 151871
 
3.4%
B 142398
 
3.1%
D 142398
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4527360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1111030
24.5%
M 858381
19.0%
Y 858381
19.0%
H 822823
18.2%
K 440078
 
9.7%
T 151871
 
3.4%
B 142398
 
3.1%
D 142398
 
3.1%
Distinct2263679
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size142.5 MiB
2025-01-27T12:40:09.852561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters20373120
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2263678 ?
Unique (%)> 99.9%

Sample

1st roww2lx-myz3
2nd rowta7z-r91q
3rd rowt5af-wgb2
4th rowsibu-9lm4
5th rowwe61-omtr
ValueCountFrequency (%)
r7kg-kbwc 2
 
< 0.1%
w2lx-myz3 1
 
< 0.1%
ta7z-r91q 1
 
< 0.1%
t5af-wgb2 1
 
< 0.1%
sibu-9lm4 1
 
< 0.1%
we61-omtr 1
 
< 0.1%
vbpl-6rrv 1
 
< 0.1%
t8ba-5cq4 1
 
< 0.1%
g5ga-lvkp 1
 
< 0.1%
z6hz-u94i 1
 
< 0.1%
Other values (2263669) 2263669
> 99.9%
2025-01-27T12:40:11.871096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2263680
 
11.1%
s 653592
 
3.2%
q 596797
 
2.9%
n 586069
 
2.9%
t 584415
 
2.9%
r 556864
 
2.7%
u 541108
 
2.7%
o 540606
 
2.7%
p 523964
 
2.6%
m 510444
 
2.5%
Other values (27) 13015581
63.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20373120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2263680
 
11.1%
s 653592
 
3.2%
q 596797
 
2.9%
n 586069
 
2.9%
t 584415
 
2.9%
r 556864
 
2.7%
u 541108
 
2.7%
o 540606
 
2.7%
p 523964
 
2.6%
m 510444
 
2.5%
Other values (27) 13015581
63.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20373120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2263680
 
11.1%
s 653592
 
3.2%
q 596797
 
2.9%
n 586069
 
2.9%
t 584415
 
2.9%
r 556864
 
2.7%
u 541108
 
2.7%
o 540606
 
2.7%
p 523964
 
2.6%
m 510444
 
2.5%
Other values (27) 13015581
63.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20373120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2263680
 
11.1%
s 653592
 
3.2%
q 596797
 
2.9%
n 586069
 
2.9%
t 584415
 
2.9%
r 556864
 
2.7%
u 541108
 
2.7%
o 540606
 
2.7%
p 523964
 
2.6%
m 510444
 
2.5%
Other values (27) 13015581
63.9%
Distinct1286063
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Memory size140.3 MiB
2025-01-27T12:40:13.039366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.0017856
Min length8

Characters and Unicode

Total characters18113482
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique892957 ?
Unique (%)39.4%

Sample

1st rowbdpr8uva
2nd rowbd59rlzo
3rd rowbd6zhjvq
4th rowbd4fv4rb
5th rowbdzeepq7
ValueCountFrequency (%)
my2nvlmz 366
 
< 0.1%
myuuybt1 326
 
< 0.1%
mydoe1rv 214
 
< 0.1%
myynozg1 189
 
< 0.1%
my75kpgs 185
 
< 0.1%
mywe8sca 174
 
< 0.1%
mytufok9 173
 
< 0.1%
mypedhpj 170
 
< 0.1%
myido9st 168
 
< 0.1%
my7z9hye 166
 
< 0.1%
Other values (1286053) 2261549
99.9%
2025-01-27T12:40:14.334787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 1357664
 
7.5%
y 1185051
 
6.5%
m 1181618
 
6.5%
h 1175853
 
6.5%
k 738173
 
4.1%
t 557984
 
3.1%
d 536785
 
3.0%
b 534800
 
3.0%
i 417937
 
2.3%
c 417813
 
2.3%
Other values (29) 10009804
55.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18113482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 1357664
 
7.5%
y 1185051
 
6.5%
m 1181618
 
6.5%
h 1175853
 
6.5%
k 738173
 
4.1%
t 557984
 
3.1%
d 536785
 
3.0%
b 534800
 
3.0%
i 417937
 
2.3%
c 417813
 
2.3%
Other values (29) 10009804
55.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18113482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 1357664
 
7.5%
y 1185051
 
6.5%
m 1181618
 
6.5%
h 1175853
 
6.5%
k 738173
 
4.1%
t 557984
 
3.1%
d 536785
 
3.0%
b 534800
 
3.0%
i 417937
 
2.3%
c 417813
 
2.3%
Other values (29) 10009804
55.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18113482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 1357664
 
7.5%
y 1185051
 
6.5%
m 1181618
 
6.5%
h 1175853
 
6.5%
k 738173
 
4.1%
t 557984
 
3.1%
d 536785
 
3.0%
b 534800
 
3.0%
i 417937
 
2.3%
c 417813
 
2.3%
Other values (29) 10009804
55.3%

is_fraud
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.2 MiB
0
2014323 
1
249357 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2263680
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2014323
89.0%
1 249357
 
11.0%

Length

2025-01-27T12:40:14.528083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T12:40:14.637298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2014323
89.0%
1 249357
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 2014323
89.0%
1 249357
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2263680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2014323
89.0%
1 249357
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2263680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2014323
89.0%
1 249357
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2263680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2014323
89.0%
1 249357
 
11.0%

mobile_verified
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
True
2262059 
False
 
1621
ValueCountFrequency (%)
True 2262059
99.9%
False 1621
 
0.1%
2025-01-27T12:40:14.725307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

num_orders_last_50days
Real number (ℝ)

High correlation  Zeros 

Distinct494
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.072645
Minimum0
Maximum2162
Zeros86058
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-01-27T12:40:14.841748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median26
Q353
95-th percentile116
Maximum2162
Range2162
Interquartile range (IQR)44

Descriptive statistics

Standard deviation42.764675
Coefficient of variation (CV)1.1232389
Kurtosis113.58359
Mean38.072645
Median Absolute Deviation (MAD)19
Skewness4.9855475
Sum86184286
Variance1828.8175
MonotonicityNot monotonic
2025-01-27T12:40:15.032938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86058
 
3.8%
1 66529
 
2.9%
2 61117
 
2.7%
3 56891
 
2.5%
4 55915
 
2.5%
5 52177
 
2.3%
6 50790
 
2.2%
7 48262
 
2.1%
8 46855
 
2.1%
9 44700
 
2.0%
Other values (484) 1694386
74.9%
ValueCountFrequency (%)
0 86058
3.8%
1 66529
2.9%
2 61117
2.7%
3 56891
2.5%
4 55915
2.5%
5 52177
2.3%
6 50790
2.2%
7 48262
2.1%
8 46855
2.1%
9 44700
2.0%
ValueCountFrequency (%)
2162 1
 
< 0.1%
2091 1
 
< 0.1%
1954 4
 
< 0.1%
1788 62
< 0.1%
1434 3
 
< 0.1%
1400 8
 
< 0.1%
1380 1
 
< 0.1%
1216 4
 
< 0.1%
1016 5
 
< 0.1%
1007 3
 
< 0.1%

num_cancelled_orders_last_50days
Real number (ℝ)

High correlation  Zeros 

Distinct103
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2775361
Minimum0
Maximum195
Zeros1485772
Zeros (%)65.6%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-01-27T12:40:15.197911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum195
Range195
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.6512157
Coefficient of variation (CV)4.4235271
Kurtosis355.85539
Mean1.2775361
Median Absolute Deviation (MAD)0
Skewness15.976265
Sum2891933
Variance31.936239
MonotonicityNot monotonic
2025-01-27T12:40:15.362272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1485772
65.6%
1 382623
 
16.9%
2 150543
 
6.7%
3 73825
 
3.3%
4 41275
 
1.8%
5 26842
 
1.2%
6 18459
 
0.8%
7 14100
 
0.6%
8 10860
 
0.5%
9 7719
 
0.3%
Other values (93) 51662
 
2.3%
ValueCountFrequency (%)
0 1485772
65.6%
1 382623
 
16.9%
2 150543
 
6.7%
3 73825
 
3.3%
4 41275
 
1.8%
5 26842
 
1.2%
6 18459
 
0.8%
7 14100
 
0.6%
8 10860
 
0.5%
9 7719
 
0.3%
ValueCountFrequency (%)
195 8
 
< 0.1%
193 1
 
< 0.1%
191 8
 
< 0.1%
183 189
< 0.1%
163 326
< 0.1%
158 7
 
< 0.1%
151 166
< 0.1%
133 21
 
< 0.1%
120 118
 
< 0.1%
119 34
 
< 0.1%

num_refund_orders_last_50days
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8286237
Minimum0
Maximum201
Zeros1318793
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-01-27T12:40:15.536396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8
Maximum201
Range201
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.2687173
Coefficient of variation (CV)3.4281067
Kurtosis205.64018
Mean1.8286237
Median Absolute Deviation (MAD)0
Skewness11.665044
Sum4139419
Variance39.296816
MonotonicityNot monotonic
2025-01-27T12:40:15.712292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1318793
58.3%
1 401182
 
17.7%
2 181128
 
8.0%
3 95941
 
4.2%
4 58999
 
2.6%
5 38966
 
1.7%
6 27527
 
1.2%
7 21140
 
0.9%
8 15532
 
0.7%
9 12683
 
0.6%
Other values (91) 91789
 
4.1%
ValueCountFrequency (%)
0 1318793
58.3%
1 401182
 
17.7%
2 181128
 
8.0%
3 95941
 
4.2%
4 58999
 
2.6%
5 38966
 
1.7%
6 27527
 
1.2%
7 21140
 
0.9%
8 15532
 
0.7%
9 12683
 
0.6%
ValueCountFrequency (%)
201 5
 
< 0.1%
174 189
< 0.1%
152 166
< 0.1%
146 326
< 0.1%
129 21
 
< 0.1%
126 12
 
< 0.1%
125 118
 
< 0.1%
122 366
< 0.1%
114 168
< 0.1%
113 18
 
< 0.1%

total_payment_last_50days
Real number (ℝ)

High correlation  Zeros 

Distinct16523
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean493.252
Minimum0
Maximum57888
Zeros54054
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2025-01-27T12:40:15.886422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.640625
Q196.3125
median295.25
Q3675
95-th percentile1611
Maximum57888
Range57888
Interquartile range (IQR)578.6875

Descriptive statistics

Standard deviation623.93933
Coefficient of variation (CV)1.2649504
Kurtosis282.54456
Mean493.252
Median Absolute Deviation (MAD)238.25
Skewness7.1085434
Sum1.1165647 × 109
Variance389300.31
MonotonicityNot monotonic
2025-01-27T12:40:16.063859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54054
 
2.4%
532 888
 
< 0.1%
549 831
 
< 0.1%
544.5 814
 
< 0.1%
545.5 802
 
< 0.1%
534.5 791
 
< 0.1%
557 765
 
< 0.1%
552 762
 
< 0.1%
868 759
 
< 0.1%
523.5 758
 
< 0.1%
Other values (16513) 2202456
97.3%
ValueCountFrequency (%)
0 54054
2.4%
0.0006322860718 1
 
< 0.1%
0.0007452964783 3
 
< 0.1%
0.000819683075 1
 
< 0.1%
0.0009436607361 1
 
< 0.1%
0.0009565353394 1
 
< 0.1%
0.0009694099426 1
 
< 0.1%
0.0009732246399 1
 
< 0.1%
0.001044273376 1
 
< 0.1%
0.001071929932 1
 
< 0.1%
ValueCountFrequency (%)
57888 2
 
< 0.1%
47488 4
 
< 0.1%
42656 4
 
< 0.1%
42560 2
 
< 0.1%
36064 1
 
< 0.1%
36032 4
 
< 0.1%
34592 1
 
< 0.1%
32992 8
< 0.1%
30816 1
 
< 0.1%
25696 11
< 0.1%

num_associated_customers
Real number (ℝ)

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9629197
Minimum1
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-01-27T12:40:16.218406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum101
Range100
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.6815391
Coefficient of variation (CV)2.2550524
Kurtosis157.12257
Mean2.9629197
Median Absolute Deviation (MAD)1
Skewness11.706847
Sum6707102
Variance44.642965
MonotonicityNot monotonic
2025-01-27T12:40:16.392424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 943958
41.7%
2 583079
25.8%
3 299639
 
13.2%
4 162940
 
7.2%
5 93642
 
4.1%
6 54211
 
2.4%
7 32915
 
1.5%
8 20867
 
0.9%
9 13534
 
0.6%
10 9904
 
0.4%
Other values (91) 48991
 
2.2%
ValueCountFrequency (%)
1 943958
41.7%
2 583079
25.8%
3 299639
 
13.2%
4 162940
 
7.2%
5 93642
 
4.1%
6 54211
 
2.4%
7 32915
 
1.5%
8 20867
 
0.9%
9 13534
 
0.6%
10 9904
 
0.4%
ValueCountFrequency (%)
101 713
 
< 0.1%
100 6274
0.3%
99 16
 
< 0.1%
98 33
 
< 0.1%
97 37
 
< 0.1%
96 55
 
< 0.1%
95 73
 
< 0.1%
94 48
 
< 0.1%
93 30
 
< 0.1%
92 59
 
< 0.1%
Distinct1277521
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Memory size17.3 MiB
Minimum2012-03-25 12:53:43
Maximum2023-05-10 20:59:37
2025-01-27T12:40:16.587552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:40:16.803441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

collect_type
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
delivery
2234928 
pickup
 
28752

Length

Max length8
Median length8
Mean length7.9745971
Min length6

Characters and Unicode

Total characters18051936
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivery
2nd rowdelivery
3rd rowdelivery
4th rowdelivery
5th rowdelivery

Common Values

ValueCountFrequency (%)
delivery 2234928
98.7%
pickup 28752
 
1.3%

Length

2025-01-27T12:40:16.974781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T12:40:17.085417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
delivery 2234928
98.7%
pickup 28752
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 4469856
24.8%
i 2263680
12.5%
d 2234928
12.4%
l 2234928
12.4%
v 2234928
12.4%
r 2234928
12.4%
y 2234928
12.4%
p 57504
 
0.3%
c 28752
 
0.2%
k 28752
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18051936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4469856
24.8%
i 2263680
12.5%
d 2234928
12.4%
l 2234928
12.4%
v 2234928
12.4%
r 2234928
12.4%
y 2234928
12.4%
p 57504
 
0.3%
c 28752
 
0.2%
k 28752
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18051936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4469856
24.8%
i 2263680
12.5%
d 2234928
12.4%
l 2234928
12.4%
v 2234928
12.4%
r 2234928
12.4%
y 2234928
12.4%
p 57504
 
0.3%
c 28752
 
0.2%
k 28752
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18051936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4469856
24.8%
i 2263680
12.5%
d 2234928
12.4%
l 2234928
12.4%
v 2234928
12.4%
r 2234928
12.4%
y 2234928
12.4%
p 57504
 
0.3%
c 28752
 
0.2%
k 28752
 
0.2%

payment_method
Categorical

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
PayOnDelivery
745300 
CybersourceCreditCard
323891 
RazerOnlineBanking
297827 
CreditCard
270146 
AFGCash
245626 
Other values (13)
380890 

Length

Max length21
Median length18
Mean length13.779122
Min length5

Characters and Unicode

Total characters31191524
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayOnDelivery
2nd rowCreditCard
3rd rowAFbKash
4th rowCreditCard
5th rowPayOnDelivery

Common Values

ValueCountFrequency (%)
PayOnDelivery 745300
32.9%
CybersourceCreditCard 323891
14.3%
RazerOnlineBanking 297827
 
13.2%
CreditCard 270146
 
11.9%
AFGCash 245626
 
10.9%
GenericCreditCard 172538
 
7.6%
AccountBalance 61774
 
2.7%
AFTNG 51538
 
2.3%
AFbKash 42173
 
1.9%
AFTrueMoney 21868
 
1.0%
Other values (8) 30999
 
1.4%

Length

2025-01-27T12:40:17.211030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
payondelivery 745300
32.9%
cybersourcecreditcard 323891
14.3%
razeronlinebanking 297827
 
13.2%
creditcard 270146
 
11.9%
afgcash 245626
 
10.9%
genericcreditcard 172538
 
7.6%
accountbalance 61774
 
2.7%
aftng 51538
 
2.3%
afbkash 42173
 
1.9%
aftruemoney 21868
 
1.0%
Other values (8) 30999
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 3984058
12.8%
r 3421167
 
11.0%
a 2542933
 
8.2%
i 2284970
 
7.3%
n 2277528
 
7.3%
C 2109852
 
6.8%
y 1859861
 
6.0%
d 1549618
 
5.0%
l 1120653
 
3.6%
O 1043127
 
3.3%
Other values (28) 8997757
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31191524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3984058
12.8%
r 3421167
 
11.0%
a 2542933
 
8.2%
i 2284970
 
7.3%
n 2277528
 
7.3%
C 2109852
 
6.8%
y 1859861
 
6.0%
d 1549618
 
5.0%
l 1120653
 
3.6%
O 1043127
 
3.3%
Other values (28) 8997757
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31191524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3984058
12.8%
r 3421167
 
11.0%
a 2542933
 
8.2%
i 2284970
 
7.3%
n 2277528
 
7.3%
C 2109852
 
6.8%
y 1859861
 
6.0%
d 1549618
 
5.0%
l 1120653
 
3.6%
O 1043127
 
3.3%
Other values (28) 8997757
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31191524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3984058
12.8%
r 3421167
 
11.0%
a 2542933
 
8.2%
i 2284970
 
7.3%
n 2277528
 
7.3%
C 2109852
 
6.8%
y 1859861
 
6.0%
d 1549618
 
5.0%
l 1120653
 
3.6%
O 1043127
 
3.3%
Other values (28) 8997757
28.8%

order_value
Real number (ℝ)

Distinct10810
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9325248
Minimum0
Maximum663
Zeros59
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2025-01-27T12:40:17.374651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1943359
Q13.2050781
median5.5078125
Q38.5078125
95-th percentile17.625
Maximum663
Range663
Interquartile range (IQR)5.3027344

Descriptive statistics

Standard deviation6.4227796
Coefficient of variation (CV)0.92647048
Kurtosis141.79779
Mean6.9325248
Median Absolute Deviation (MAD)2.546875
Skewness5.7110195
Sum15693018
Variance41.252098
MonotonicityNot monotonic
2025-01-27T12:40:17.598363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.421875 1442
 
0.1%
5.3984375 1400
 
0.1%
5.40625 1399
 
0.1%
5.37109375 1373
 
0.1%
5.47265625 1360
 
0.1%
5.50390625 1356
 
0.1%
5.41796875 1338
 
0.1%
5.35546875 1325
 
0.1%
5.4296875 1324
 
0.1%
5.3203125 1312
 
0.1%
Other values (10800) 2250051
99.4%
ValueCountFrequency (%)
0 59
< 0.1%
0.000335931778 1
 
< 0.1%
0.000342130661 1
 
< 0.1%
0.001029968262 1
 
< 0.1%
0.003168106079 1
 
< 0.1%
0.003253936768 1
 
< 0.1%
0.004165649414 3
 
< 0.1%
0.004173278809 2
 
< 0.1%
0.004219055176 1
 
< 0.1%
0.00422668457 3
 
< 0.1%
ValueCountFrequency (%)
663 1
< 0.1%
517.5 1
< 0.1%
418.25 1
< 0.1%
320.25 1
< 0.1%
311.75 1
< 0.1%
295.25 1
< 0.1%
280.5 1
< 0.1%
267.25 1
< 0.1%
264.25 1
< 0.1%
259.5 1
< 0.1%

num_items_ordered
Real number (ℝ)

Distinct189
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6648806
Minimum1
Maximum253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-01-27T12:40:17.800654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum253
Range252
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.7050567
Coefficient of variation (CV)1.2838226
Kurtosis162.20211
Mean3.6648806
Median Absolute Deviation (MAD)1
Skewness8.2722107
Sum8296117
Variance22.137559
MonotonicityNot monotonic
2025-01-27T12:40:17.995959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 607875
26.9%
2 592534
26.2%
3 340884
15.1%
4 235372
 
10.4%
5 120913
 
5.3%
6 100114
 
4.4%
7 50334
 
2.2%
8 49524
 
2.2%
10 29074
 
1.3%
9 25835
 
1.1%
Other values (179) 111221
 
4.9%
ValueCountFrequency (%)
1 607875
26.9%
2 592534
26.2%
3 340884
15.1%
4 235372
 
10.4%
5 120913
 
5.3%
6 100114
 
4.4%
7 50334
 
2.2%
8 49524
 
2.2%
9 25835
 
1.1%
10 29074
 
1.3%
ValueCountFrequency (%)
253 1
< 0.1%
250 1
< 0.1%
243 1
< 0.1%
240 2
< 0.1%
238 1
< 0.1%
233 1
< 0.1%
226 1
< 0.1%
225 1
< 0.1%
220 2
< 0.1%
215 1
< 0.1%

refund_value
Real number (ℝ)

Zeros 

Distinct3641
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2338936
Minimum0
Maximum518
Zeros876848
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2025-01-27T12:40:18.182486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.93017578
Q32.9003906
95-th percentile8.8125
Maximum518
Range518
Interquartile range (IQR)2.9003906

Descriptive statistics

Standard deviation3.876858
Coefficient of variation (CV)1.7354712
Kurtosis243.06848
Mean2.2338936
Median Absolute Deviation (MAD)0.93017578
Skewness6.7317829
Sum5056820.2
Variance15.030028
MonotonicityNot monotonic
2025-01-27T12:40:18.349993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 876848
38.7%
0.8500976562 6796
 
0.3%
0.75 5695
 
0.3%
0.83984375 5588
 
0.2%
0.2700195312 5449
 
0.2%
0.9599609375 5251
 
0.2%
0.740234375 5054
 
0.2%
0.6401367188 4977
 
0.2%
1.700195312 4696
 
0.2%
0.5400390625 4666
 
0.2%
Other values (3631) 1338660
59.1%
ValueCountFrequency (%)
0 876848
38.7%
0.01000213623 6
 
< 0.1%
0.02000427246 56
 
< 0.1%
0.0299987793 40
 
< 0.1%
0.04000854492 180
 
< 0.1%
0.04998779297 91
 
< 0.1%
0.05999755859 165
 
< 0.1%
0.07000732422 203
 
< 0.1%
0.08001708984 1388
 
0.1%
0.09002685547 1234
 
0.1%
ValueCountFrequency (%)
518 1
< 0.1%
313 1
< 0.1%
281.75 1
< 0.1%
234.25 1
< 0.1%
214.625 1
< 0.1%
209.25 1
< 0.1%
196.75 1
< 0.1%
177.75 1
< 0.1%
166.875 1
< 0.1%
156.5 1
< 0.1%
Distinct181
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.3 MiB
Minimum2022-11-12 00:00:00
Maximum2023-05-11 00:00:00
2025-01-27T12:40:18.498888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:40:18.686280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-01-27T12:39:53.314015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:24.494856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:28.148549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:31.918662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:35.567434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:39.924352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:45.191609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:49.236567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:53.816795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:24.953050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:28.569953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:32.382907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:35.980257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:40.706740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:45.734670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:49.733112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:54.257008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:25.516366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:29.052373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:32.898636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:36.434203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:41.339224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:46.252112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:50.201790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:54.716770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:25.966372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:29.512543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:33.360963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:36.861634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:42.066542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:46.801340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:50.719443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:55.259563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:26.401909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:30.039459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:33.823849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:37.382888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:42.820519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:47.345853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:51.265659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:55.740373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:26.837409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:30.596086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:34.261219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:37.938844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:43.423122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:47.809717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:51.792232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:56.211462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:27.256977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:31.070514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:34.682128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:38.594849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:44.060646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:48.290772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:52.286339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:56.653131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:27.728831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:31.495865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:35.120022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:39.223573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:44.630473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:48.809741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-27T12:39:52.822857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-27T12:40:18.839612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
collect_typecountry_codeis_fraudmobile_verifiednum_associated_customersnum_cancelled_orders_last_50daysnum_items_orderednum_orders_last_50daysnum_refund_orders_last_50daysorder_valuepayment_methodrefund_valuetotal_payment_last_50days
collect_type1.0000.0830.0630.0010.0280.0170.0040.0430.0100.0040.1340.0050.020
country_code0.0831.0000.2860.0060.0390.0290.0290.0230.0280.0070.6110.0100.016
is_fraud0.0630.2861.0000.0000.2110.0280.0720.0160.0360.0410.2050.0070.010
mobile_verified0.0010.0060.0001.0000.0010.3320.0000.0320.2150.0000.0090.0000.000
num_associated_customers0.0280.0390.2110.0011.0000.190-0.0140.1480.162-0.0830.021-0.0380.072
num_cancelled_orders_last_50days0.0170.0290.0280.3320.1901.000-0.0630.4760.585-0.1060.0440.0700.312
num_items_ordered0.0040.0290.0720.000-0.014-0.0631.0000.032-0.0180.4960.016-0.0830.128
num_orders_last_50days0.0430.0230.0160.0320.1480.4760.0321.0000.5470.0140.0200.0860.901
num_refund_orders_last_50days0.0100.0280.0360.2150.1620.585-0.0180.5471.000-0.0600.0410.1420.433
order_value0.0040.0070.0410.000-0.083-0.1060.4960.014-0.0601.0000.0160.0480.219
payment_method0.1340.6110.2050.0090.0210.0440.0160.0200.0410.0161.0000.0140.024
refund_value0.0050.0100.0070.000-0.0380.070-0.0830.0860.1420.0480.0141.0000.123
total_payment_last_50days0.0200.0160.0100.0000.0720.3120.1280.9010.4330.2190.0240.1231.000

Missing values

2025-01-27T12:39:57.092656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-27T12:40:00.297889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

country_codeorder_idcustomer_idis_fraudmobile_verifiednum_orders_last_50daysnum_cancelled_orders_last_50daysnum_refund_orders_last_50daystotal_payment_last_50daysnum_associated_customersfirst_order_datetimecollect_typepayment_methodorder_valuenum_items_orderedrefund_valueorder_date
0BDw2lx-myz3bdpr8uva0True0000.000032022-08-13 03:53:52deliveryPayOnDelivery8.66406290.8701172023-04-08
1BDta7z-r91qbd59rlzo0True700228.000042022-05-08 14:29:19deliveryCreditCard21.85937542.2792972023-02-13
2BDt5af-wgb2bd6zhjvq0True41045.687522021-08-25 07:47:00deliveryAFbKash7.12500012.3496092023-03-06
3BDsibu-9lm4bd4fv4rb0True1903279.750052021-12-06 13:53:22deliveryCreditCard4.53515650.1500242023-01-29
4BDwe61-omtrbdzeepq70True3064107.062552020-07-04 11:45:39deliveryPayOnDelivery3.01171913.7500002023-01-16
5BDvbpl-6rrvbdofafbw0True892101341.000012021-09-18 12:59:00deliveryAccountBalance12.32812584.9218752023-04-27
6BDt8ba-5cq4bdp3nilt0True1000.000032021-08-10 13:27:40deliveryPayOnDelivery3.36328123.8007812023-04-06
7BDg5ga-lvkpbdw1zcp10True110379.812512022-10-09 07:27:23deliveryPayOnDelivery0.74853514.5000002022-11-16
8BDz6hz-u94ibd8wyt3z0True3500230.250032022-11-15 01:12:19deliveryPayOnDelivery1.52148430.1700442022-11-15
9BDh94y-6rwsd9eth7xe0True8213949.500012017-08-05 06:25:01deliveryCreditCard2.43945320.5200202022-12-15
country_codeorder_idcustomer_idis_fraudmobile_verifiednum_orders_last_50daysnum_cancelled_orders_last_50daysnum_refund_orders_last_50daystotal_payment_last_50daysnum_associated_customersfirst_order_datetimecollect_typepayment_methodorder_valuenum_items_orderedrefund_valueorder_date
2263670PKv2ni-wclgpk9n9slb1True0000.76464862023-04-06 13:05:23deliveryJazzCashWallet0.84130960.02023-04-06
2263671PKl7rh-7bnlpkhws5xx1True100065.93750012019-11-05 19:14:15deliveryPayOnDelivery3.85156220.02022-12-18
2263672PKmmth-nzpxpkqoj84m1True60062.34375032022-11-23 14:34:31deliveryInvoice3.84960960.02023-05-06
2263673PKn7ng-hp88pky9xbuu1True51017.98437522021-06-26 07:18:17deliveryPayOnDelivery1.79101610.02023-02-19
2263674PKsik4-ppxapkyxo3om1True1910240.12500042019-07-17 09:01:56deliveryGenericCreditCard11.523438290.02022-12-14
2263675PKs2go-o04opkn8lcvq1True0000.00000012022-03-07 09:33:11deliveryGenericCreditCard3.73242210.02022-12-27
2263676PKwepx-o8p9pkmv7mxc1True20012.46093832021-01-16 15:59:39deliveryPayOnDelivery1.31054710.02023-04-10
2263677PKjvd2-zdf0pkdgo2zl1True3008.60156232019-08-04 14:20:44deliveryPayOnDelivery1.41406220.02023-05-05
2263678PKkca0-drm6pkovsmz71True2920204.62500022021-12-02 16:24:04deliveryPayOnDelivery7.31640640.02023-04-13
2263679PKu7w3-gfj3pkw0n07l1True40011.45312512022-06-04 08:33:09deliveryPayOnDelivery1.35156220.02023-01-21